Sentiment Analysis is one of the most wanted and used NLP techniques. Companies like to see what their customers are talking about – like if there’s a new product launch then what’s the feedback about it. Whereever you’ve got Natural Language – like Social Media, Community Pages, Customer Support – Sentiment Analysis as a technique has found its home there.

While the technique itself is highly wanted, Sentiment Analysis is one of the NLP fields that’s far from super-accurate and the reason being is a lot of ways Humans talk. One of the aspects of it is called Valence Shifters like Negation that can flip the polarity of a sentence with one word.

“I’m happy” -> Positive
“I’m not happy” -> Negative

Because of this, a lot of out-of-box Sentiment analysis packages and libraries fail at tasks like this. Kudos to Tyler Rinker’s sentimentr R package that handles this scenario very well. sentimentr is a lexicon-based Sentiment Analysis Package that’s one of the best out-of-box sentiment analysis solution (given you don’t want to build a Sentiment Classification or you don’t want to use a Paid API like Google Cloud API).